In the field of artificially intelligent computer systems capable of answering questions posed in natural language, cognitive question answering (QA) systems (such as the IBM Watson™ artificially intelligent computer system or and other natural language question answering systems) process questions posed in natural language to determine answers and associated confidence scores based on knowledge acquired by the QA system. In operation, users submit one or more questions through a front-end application user interface (UI) or application programming interface (API) to the QA system where the questions are processed to generate answers that are returned to the user(s). In order to prepare an answer, traditional QA systems use a named entity recognition (NER) process (also known as entity identification, entity chunking and entity extraction) to analyze textual information in a large knowledge database (or “corpus”) by locating and classifying elements in the textual information into pre-defined categories, such as the names of persons, organizations, locations, expressions of times, quantities, monetary values, percentages, etc. However, the NER processes used in different industry domains often results in different or conflicting named entity extraction results, depending on the contexts of the different industry domains. For example, the same entity may be identified in different industry domains with the same identifier or with different identifiers, again depending on the contexts of the different industry domains. While certain named entity extraction schemes have been proposed which extract the named entities of a class from a large amount of corpus and into a dictionary for a given industry domain, such schemes are not well suited for contextualizing the recognition named entities extracted from different industry domains. As a result, the existing solutions for efficiently identifying and recognizing entity relationships across different industry domain dictionaries are extremely difficult at a practical level.